Face Image Quality Assessment: A Literature Survey
- URL: http://arxiv.org/abs/2009.01103v3
- Date: Mon, 25 Oct 2021 13:06:18 GMT
- Title: Face Image Quality Assessment: A Literature Survey
- Authors: Torsten Schlett, Christian Rathgeb, Olaf Henniger, Javier Galbally,
Julian Fierrez, Christoph Busch
- Abstract summary: This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input.
A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches.
- Score: 16.647739693192236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The performance of face analysis and recognition systems depends on the
quality of the acquired face data, which is influenced by numerous factors.
Automatically assessing the quality of face data in terms of biometric utility
can thus be useful to detect low-quality data and make decisions accordingly.
This survey provides an overview of the face image quality assessment
literature, which predominantly focuses on visible wavelength face image input.
A trend towards deep learning based methods is observed, including notable
conceptual differences among the recent approaches, such as the integration of
quality assessment into face recognition models. Besides image selection, face
image quality assessment can also be used in a variety of other application
scenarios, which are discussed herein. Open issues and challenges are pointed
out, i.a. highlighting the importance of comparability for algorithm
evaluations, and the challenge for future work to create deep learning
approaches that are interpretable in addition to providing accurate utility
predictions.
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